Slow-thinking Large Language Models (LLMs) have demonstrated strong reasoning capabilities but often suffer from severe hallucinations due to an inability to recognize their knowledge boundaries.Existing Reinforcement Learning (RL) approaches typically rely on outcomeoriented rewards, which can inadvertently reinforce fabricated reasoning paths when the final answer is correct.To address this, we propose Knowledge-enhanced RL, KnowRL, a framework that integrates factual supervision directly into the reasoning process.By decomposing the chain of thought into atomic facts and verifying them against the corresponding ground-truth knowledge, KnowRL performs fine-grained checks to encourage models to reason faithfully.Crucially, this processoriented supervision teaches the model to identify its knowledge boundaries, learning to say "I don't know" instead of fabricating answers when information is missing.Experimental results demonstrate that KnowRL effectively mitigates hallucinations-reducing the Incorrect Rate on SimpleQA by 20.3% for distillationbased slow-thinking models while maintaining strong performance on complex reasoning benchmarks like GPQA and AIME 2025.Furthermore, our method shows robust transferability to out-of-distribution tasks, indicating that the model learns a generalizable verification behavior 1 .

Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.